Differential co-expression and regulation analyses reveal different mechanisms underlying major depressive disorder and subsyndromal symptomatic depression

BackgroundRecent depression research has revealed a growing awareness of how to best classify depression into depressive subtypes. Appropriately subtyping depression can lead to identification of subtypes that are more responsive to current pharmacological treatment and aid in separating out depressed patients in which current antidepressants are not particularly effective.Differential co-expression analysis (DCEA) and differential regulation analysis (DRA) were applied to compare the transcriptomic profiles of peripheral blood lymphocytes from patients with two depressive subtypes: major depressive disorder (MDD) and subsyndromal symptomatic depression (SSD).ResultsSix differentially regulated genes (DRGs) (FOSL1, SRF, JUN, TFAP4, SOX9, and HLF) and 16 transcription factor-to-target differentially co-expressed gene links or pairs (TF2target DCLs) appear to be the key differential factors in MDD; in contrast, one DRG (PATZ1) and eight TF2target DCLs appear to be the key differential factors in SSD. There was no overlap between the MDD target genes and SSD target genes. Venlafaxine (Efexor™, Effexor™) appears to have a significant effect on the gene expression profile of MDD patients but no significant effect on the gene expression profile of SSD patients.ConclusionDCEA and DRA revealed no apparent similarities between the differential regulatory processes underlying MDD and SSD. This bioinformatic analysis may provide novel insights that can support future antidepressant R&D efforts.

[1]  R. Villanueva Neurobiology of Major Depressive Disorder , 2013, Neural plasticity.

[2]  C. Tsigos,et al.  Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. , 2002, Journal of psychosomatic research.

[3]  G. Freedman,et al.  Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010 , 2013, PLoS medicine.

[4]  Julio Licinio,et al.  From monoamines to genomic targets: a paradigm shift for drug discovery in depression , 2004, Nature Reviews Drug Discovery.

[5]  R. Baldessarini,et al.  Overview of antidepressant treatment of bipolar depression. , 2013, The international journal of neuropsychopharmacology.

[6]  E O Voit,et al.  Effects of Dopamine and Glutamate on Synaptic Plasticity: A Computational Modeling Approach for Drug Abuse as Comorbidity in Mood Disorders , 2011, Pharmacopsychiatry.

[7]  F. Holsboer,et al.  Neuropeptide receptor ligands as drugs for psychiatric diseases: the end of the beginning? , 2012, Nature Reviews Drug Discovery.

[8]  Zhongming Zhao,et al.  DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression , 2013, PloS one.

[9]  A. Cleare,et al.  What happens to patients with treatment-resistant depression? A systematic review of medium to long term outcome studies. , 2009, Journal of affective disorders.

[10]  Eric J. Nestler,et al.  The molecular neurobiology of depression , 2008, Nature.

[11]  Wu Hong,et al.  Blood-Based Gene Expression Profiles Models for Classification of Subsyndromal Symptomatic Depression and Major Depressive Disorder , 2012, PloS one.

[12]  A. G. de la Fuente From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. , 2010, Trends in genetics : TIG.

[13]  Abraham D. Flaxman,et al.  The Epidemiological Modelling of Major Depressive Disorder: Application for the Global Burden of Disease Study 2010 , 2013, PloS one.

[14]  Antonio Reverter,et al.  A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation , 2009, PLoS Comput. Biol..

[15]  Y. Yang,et al.  Proteomics reveals energy and glutathione metabolic dysregulation in the prefrontal cortex of a rat model of depression , 2013, Neuroscience.

[16]  Hui Yu,et al.  Bioinformatics Applications Note Gene Expression Dcgl: an R Package for Identifying Differentially Coexpressed Genes and Links from Gene Expression Microarray Data , 2022 .

[17]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .

[18]  D. Mohr,et al.  Major depressive disorder , 2016, Nature Reviews Disease Primers.

[19]  R. Duman,et al.  Signaling pathways underlying the pathophysiology and treatment of depression: novel mechanisms for rapid-acting agents , 2012, Trends in Neurosciences.

[20]  W. Drevets,et al.  The cellular neurobiology of depression , 2001, Nature Medicine.

[21]  R. Kessler,et al.  Data-driven subtypes of major depressive disorder: a systematic review , 2012, BMC Medicine.

[22]  Eduard Vieta,et al.  Cytokine-induced depression: current status and novel targets for depression therapy. , 2014, CNS & neurological disorders drug targets.

[23]  M. Barrot,et al.  Neurobiology of Depression , 2002, Neuron.

[24]  Peng Xie,et al.  Metabolomic identification of molecular changes associated with stress resilience in the chronic mild stress rat model of depression , 2013, Metabolomics.

[25]  Peng Xie,et al.  Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder* , 2012, Molecular & Cellular Proteomics.

[26]  Peng Xie,et al.  Identification of suitable plasma-based reference genes for miRNAome analysis of major depressive disorder. , 2014, Journal of affective disorders.

[27]  L. Carboni,et al.  Peripheral Biomarkers in Animal Models of Major Depressive Disorder , 2013, Disease markers.

[28]  Peng Xie,et al.  Urinary peptidomics identifies potential biomarkers for major depressive disorder , 2014, Psychiatry Research.

[29]  S Nassir Ghaemi,et al.  Solving the antidepressant efficacy question: effect sizes in major depressive disorder. , 2011, Clinical therapeutics.

[30]  Jürgen Zschocke,et al.  Interrelation of Major Depression and Antidepressant Transcriptomics , 2012 .

[31]  G. Hasler,et al.  PATHOPHYSIOLOGY OF DEPRESSION: DO WE HAVE ANY SOLID EVIDENCE OF INTEREST TO CLINICIANS? , 2010, World psychiatry : official journal of the World Psychiatric Association.

[32]  Michael Griffin,et al.  Gene co-expression network topology provides a framework for molecular characterization of cellular state , 2004, Bioinform..

[33]  Brian P. Dalrymple,et al.  Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data , 2010, Bioinform..

[34]  Dermot Walsh,et al.  Neurotransmitter and neuromodulator genes associated with a history of depressive symptoms in individuals with alcohol dependence. , 2011, Alcoholism, clinical and experimental research.

[35]  Andreas Rytz,et al.  The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data , 2002, BMC Bioinformatics.

[36]  R. DeRubeis,et al.  Antidepressant drug effects and depression severity: a patient-level meta-analysis. , 2010, JAMA.